New energy automobile charging control method and system combined with battery health monitoring

Abstract

The invention discloses a new energy automobile charging control method and system combined with battery health monitoring, and relates to the technical field of charging control, and the method comprises the steps: collecting the working state of a target battery, and carrying out the health assessment; extracting a battery sample, positioning a health stage, and generating a life cycle position; performing progressive charging control on the target battery to obtain charging parameters; health assessment, decline analysis and stage division are carried out, and a predicted life cycle position is generated; and carrying out preamble judgment on a life cycle position, and analyzing preamble influence factors to generate a maintenance suggestion. The technical problems that in the prior art, the battery state cannot be comprehensively monitored and managed, so that the charging potential safety hazard of the battery is high, and the service life is short are solved, the full-process management from state collection, health assessment and charging control to fault prediction and maintenance suggestion generation of the battery is achieved, and the management efficiency of the battery is improved. The charging safety of the battery is improved; and the overall service life of the battery is prolonged.

Description

Technical Field

[0001] The present invention relates to the field of charging control technology, and in particular to a new energy vehicle charging control method and system combined with battery health monitoring. Background Art

[0002] In the field of new energy vehicles, as their market share continues to rise, the performance assurance of batteries as core components has become a key concern. Traditional battery management technology has become increasingly insufficient in the face of complex and changing usage environments and user habits. It can only perform simple power monitoring and lacks the ability to deeply analyze and accurately evaluate the health status of the battery. In terms of charging control, a fixed charging mode is usually adopted, and the differences in battery characteristics at different stages of the life cycle are not fully considered. This not only reduces the charging efficiency, but also easily causes safety problems such as battery overheating and overcharging, which seriously affects the battery's service life. At the same time, there is a lack of effective monitoring and analysis methods for the long-term impact of driving behavior and charging habits on batteries, making it difficult to predict potential failures in advance and provide targeted maintenance suggestions, which cannot meet the needs of efficient and safe operation of new energy vehicles.

[0003] The existing technology is unable to fully monitor and manage the battery status, which leads to technical problems such as high battery charging safety risks and short service life. Summary of the invention

[0004] The present application provides a new energy vehicle charging control method and system combined with battery health monitoring, which is used to solve the technical problem that the prior art cannot comprehensively monitor and manage the battery status, resulting in high battery charging safety risks and short service life.

[0005] In view of the above problems, the present application provides a new energy vehicle charging control method and system combined with battery health monitoring.

[0006] In a first aspect of the present application, a new energy vehicle charging control method combined with battery health monitoring is provided, the method comprising: Collect the working status of the target battery, perform a health assessment on the working status based on the status assessment channel, perform a decay analysis and stage division on the generated health status, and obtain a health stage; extract battery samples of the same type as the target battery, evaluate the life cycle of the battery samples from the beginning to the end of life, locate the health stage in the life cycle, and generate a life cycle position; perform progressive charging control on the target battery according to the life cycle position to obtain charging parameters; collect driving behavior records and charging records for health assessment, decay analysis and stage division, locate the generated predicted health stage in the life cycle, and generate a predicted life cycle position; perform a pre-conditioning judgment on the predicted life cycle position at the life cycle position, and if a pre-conditioning result is obtained, analyze the pre-conditioning influencing factors to generate maintenance suggestions.

[0007] The second aspect of the present application provides a new energy vehicle charging control system combined with battery health monitoring, the system comprising: A health stage acquisition module is used to collect the working status of the target battery, perform a health assessment on the working status based on the status assessment channel, perform a decay analysis and stage division on the generated health status, and obtain a health stage; a life cycle position generation module is used to extract battery samples of the same type as the target battery, evaluate the life cycle of the battery samples from the beginning to the end of life, locate the health stage in the life cycle, and generate a life cycle position; a charging parameter acquisition module is used to perform progressive charging control on the target battery according to the life cycle position to obtain charging parameters; a predicted life cycle position generation module is used to collect driving behavior records and charging records for health assessment, decay analysis and stage division, locate the generated predicted health stage in the life cycle, and generate a predicted life cycle position; a maintenance suggestion generation module is used to perform a preconditioning judgment on the predicted life cycle position at the life cycle position, and if a preconditioning result is obtained, analyze the preconditioning influencing factors to generate maintenance suggestions.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: Collect the working status of the target battery, conduct health assessment, perform decay analysis and stage division on the generated health status, and obtain the health stage; extract battery samples of the same type as the target battery, evaluate the life cycle of the battery sample from the beginning to the end of life, perform positioning, and generate the life cycle position; perform progressive charging control on the target battery according to the life cycle position to obtain charging parameters; collect driving behavior records and charging records for health assessment, decay analysis and stage division to generate a predicted life cycle position; perform a pre-conditioning judgment at the life cycle position, and if a pre-conditioning result is obtained, analyze the pre-conditioning influencing factors to generate maintenance suggestions. The whole process management of the battery from status collection, health assessment, charging control to fault prediction and maintenance suggestion generation is achieved, and the technical effect of improving battery charging safety and overall service life is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of a new energy vehicle charging control method combined with battery health monitoring provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a new energy vehicle charging control system combined with battery health monitoring provided in an embodiment of the present application.

[0011] Explanation of reference numerals: health stage acquisition module 10 , life cycle position generation module 20 , charging parameter acquisition module 30 , predicted life cycle position generation module 40 , maintenance suggestion generation module 50 . DETAILED DESCRIPTION

[0012] The present application provides a new energy vehicle charging control method and system combined with battery health monitoring, which is used to solve the technical problem that the prior art cannot comprehensively monitor and manage the battery status, resulting in high battery charging safety risks and short service life.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides a new energy vehicle charging control method combined with battery health monitoring, the method comprising: Step S100: collecting the working state of the target battery, performing health assessment on the working state based on the state assessment channel, performing decay analysis and stage division on the generated health state, and obtaining a health stage.

[0015] Specifically, during the operation of new energy vehicles, the working status of the target battery is collected in real time through high-precision sensors installed in the battery management system. These sensors accurately measure the voltage of the battery, and its value reflects the instantaneous power state of the battery; the current data reflects the charge and discharge rate of the battery, which is crucial for judging the battery's workload; the temperature parameter is a key factor affecting battery performance and life, and too high or too low temperature may cause the battery performance to decline; the remaining capacity (SoC) intuitively shows the proportion of the current power stored in the battery, which is an important indicator for measuring the battery's endurance; the measurement of internal resistance helps to understand the chemical reaction efficiency and power transmission loss inside the battery. After collecting these parameter combinations, they are input into the state evaluation channel built based on the neural network architecture. After training with a large number of state samples and health state samples of the same type of batteries under different working conditions, the channel can quickly and accurately perform health evaluation on the input working state and output the current health state of the battery. Then, the data analysis algorithm is used to extract the decay characteristics of the health state, analyze the key decay indicators such as the decay rate of battery capacity over time and the growth trend of internal resistance, and generate decay data through the trend analysis method of time series fitting. Finally, based on pre-set degradation constraints, such as battery capacity decay to a specific threshold, internal resistance growth exceeding a specified range, etc., the degradation data is identified and classified, so that the battery health status is clearly divided into different health stages, such as healthy, mild degradation, moderate degradation and severe degradation, etc., providing key basis for subsequent battery management and charging control strategies.

[0016] Step S200: extracting battery samples of the same type as the target battery, evaluating the life cycle of the battery samples from the beginning to the end of life, locating the health stage in the life cycle, and generating a life cycle position.

[0017] Specifically, the extraction operation of battery samples is first started. In a special battery database, a large number of battery samples of the same type are accurately screened out based on the key information such as the model, specification, and chemical composition of the target battery. These samples are accompanied by rich data records, covering the entire process from the initial stage of life when they are just out of the factory, when their various performances are in the best state, to the end of life stage when they gradually decline after long-term use and multiple charge and discharge cycles until they cannot meet the normal use requirements. Using the support vector regression (SVR) algorithm, a large number of battery sample data of the same type as the target battery are first collected, including key performance parameters such as capacity, internal resistance, charge and discharge efficiency of each sample at different time points or charge and discharge cycle numbers as feature vectors, and the corresponding time or cycle number as labels. The SVR model is trained using these data, and by finding an optimal hyperplane, the error of as many sample data points as possible from the hyperplane is minimized, thereby establishing a nonlinear regression model between battery performance indicators and time or cycle number. When evaluating the life cycle of a battery sample, the new battery sample data is input into the trained SVR model, and the model will predict the performance changes of the battery sample at different time points or cycles, and then determine the approximate process from the beginning of life to the end of life. When locating the health stage, the predicted performance value is compared with the pre-set health stage threshold. For example, when the capacity is lower than a certain threshold and the internal resistance is higher than a specific value, the battery is judged to be in the decline stage, and its specific health stage position is further determined within the determined life cycle framework, thereby generating accurate life cycle position information, providing a key basis for subsequent charging control strategies.

[0018] Step S300: performing progressive charging control on the target battery according to the life cycle position to obtain charging parameters.

[0019] Specifically, first extract each charging item in the pre-charging parameters, such as charging current, charging voltage, charging time, etc. Then, configure the supplementary order for these charging items according to the determined life cycle position. The specific approach is to simulate the positioning of the battery sample according to the life cycle position to obtain a simulated battery sample with the same life cycle position. Perform a performance simulation based on the charging item control on the simulated battery sample, generate different charging item control strategies, extract the first control strategy and perform performance simulation to obtain the first simulated performance, and then generate subsequent simulated performances based on the first simulated performance, and finally combine all simulated performances to obtain the simulated charging performance. Evaluate according to the simulated charging performance, map the charging items from high to low according to the performance, and determine the supplementary order of the charging items. After that, dynamically control the charging items and test their optimized performance, compare them with the ideal charging performance of the battery sample at the beginning of its life, identify the insufficient performance, and quantify the charging parameters to obtain the parameters to be supplemented. The parameters to be supplemented are distributed to the corresponding charging items according to the replenishment order of the charging items, and the parameters of the items to be supplemented are generated. Finally, the parameters of the items to be supplemented are controlled in sequence based on the control limits of the charging items. When the cumulative control performance meets the ideal performance, the final charging parameters are determined to achieve progressive charging control of the target battery, so as to improve the charging efficiency and extend the battery life.

[0020] Step S400: collecting driving behavior records and charging records for health assessment, decay analysis and stage division, locating the generated predicted health stage in the life cycle, and generating a predicted life cycle position.

[0021] Specifically, a data acquisition module is set up to continuously collect driving behavior records and charging records through the vehicle's sensor network and charging system interface. In terms of driving behavior records, for example, the acceleration and deceleration data of the vehicle are obtained through the accelerometer to determine the frequency and intensity of sudden acceleration and sudden braking; GPS or mileage sensors are used to record mileage and driving route information, and analyze the driving time and frequency under different road conditions. Charging record collection includes parameters such as the start time, end time, charging power, charging current and voltage of each charge. The collected data is transmitted to the vehicle's data analysis system. Based on the pre-built machine learning model, the long short-term memory network (LSTM) performs health assessment. For driving behavior data, the model learns the weight of the impact of different driving behavior patterns on battery health. For example, frequent sudden acceleration may cause changes in the internal structural stress of the battery, thereby affecting the battery life. The model deducts points or performs weighted assessment on battery health based on this. For charging records, the fluctuations of charging current and voltage and the number of overcharges and overdischarges are analyzed. These factors will also be converted into indicators of impact on battery health in the model.

[0022] In the decay analysis stage, the time series analysis method is used to observe the trend of battery health indicators changing with time and driving and charging behaviors. By monitoring the slope of the decay curve of battery capacity over time and combining the influence of driving and charging behavior data, it is determined whether the battery decay rate is abnormal. According to the results of health assessment and decay analysis, the stages are divided according to the set thresholds and rules, such as dividing the battery health status into good, mild decay, moderate decay and severe decay stages. Finally, after determining the predicted health stage, the previously established battery sample life cycle model is also used to match and locate the predicted health stage with the performance characteristics of the battery sample throughout the life cycle, thereby generating a predicted life cycle position. This location information helps plan battery maintenance and charging strategies in advance to ensure the reliable operation of the battery and extend its service life.

[0023] Step S500: performing a preconditioning judgment on the predicted life cycle position at the life cycle position, and if a preconditioning result is obtained, analyzing preconditioning influencing factors to generate maintenance suggestions.

[0024] Specifically, the predicted life cycle position is compared and analyzed with the current actual life cycle position. By comparing the differences in key indicators between the two, such as the degree of battery capacity attenuation, changes in internal resistance, and charge and discharge efficiency, it is determined whether the predicted life cycle position is in the pre-stage of the current life cycle position. If it is determined that there is a pre-stage result, it means that the battery is about to face some performance changes or potential failure risks. At this time, the relevant pre-stage influencing factors are deeply explored. On the one hand, starting from the driving behavior records, analyze the impact of behaviors such as the frequency of sudden acceleration and sudden braking, long-term high-speed driving or driving in extreme environments on the battery; on the other hand, a detailed analysis of the charging records is carried out to check whether there are bad charging habits such as frequent fast charging, overcharging, and charging in high or low temperature environments. For example, if it is found that sudden acceleration is frequent during driving recently and fast charging is often performed in a high temperature environment, combined with the characteristics of the battery and existing research data, analyze how these factors accelerate battery aging and performance degradation. Based on this, generate targeted maintenance suggestions, such as reminding the driver to reduce sudden acceleration operations, try to choose regular charging in a suitable temperature environment, and regularly perform deep discharge and full charging of the battery to calibrate the battery capacity, thereby effectively extending the battery life and ensuring the normal operation of new energy vehicles.

[0025] In a possible implementation, step S100 further includes: Step S110: Acquire a state sample and a health state sample of the battery sample.

[0026] Step S120: training the state samples and health state samples based on a neural network architecture to establish the state assessment channel.

[0027] Step S130: input the working status into the status evaluation channel to identify the health status and obtain the health status.

[0028] Step S140: extracting decay features of the health status, performing trend analysis of time series fitting based on the decay features, and generating decay data.

[0029] Step S150: Identify the decay data based on the decay constraint to obtain the healthy stage.

[0030] Specifically, we first conduct data collection. We carefully select the status samples and health status samples of battery samples from a large number of historical data of the same type of batteries. The status samples cover the changes in multiple dimensions of physical parameters such as voltage, current, temperature, internal resistance, etc. of the battery under different working conditions, while the health status samples clearly record the actual health status of the battery at the corresponding moment, such as whether there is too fast capacity decay, reduced charging and discharging efficiency, etc.

[0031] Using a long short-term memory network (LSTM), the state samples and health state samples of the obtained battery samples are first preprocessed. The voltage, current, temperature, internal resistance and other parameters in the state samples are normalized so that their values ​​range from 0 to 1, which is convenient for LSTM network processing. Then the LSTM network structure is constructed, and the appropriate number of hidden layers (such as 2 layers) and the number of neurons in each layer (such as 128) are set. The input layer receives the normalized state sample data. After being processed by the LSTM layer, its unique gating mechanism can effectively capture the dynamic change information of the battery state data over time, and then the processed information is mapped to the output space of the health state through the fully connected layer. The loss function is defined as the mean square error (MSE), which is used to measure the difference between the predicted health state and the actual health state. The Adam optimizer is selected, and its feature of adaptively adjusting the learning rate helps to train the network faster and more stably. During the training process, the divided training set samples are input into the LSTM network in batches (such as 64 samples per batch), the loss value is calculated, and the weight and bias parameters of the network are updated through the Adam optimizer. After each training epoch (a complete traversal of the training set), the performance of the model is evaluated on the validation set, and the MSE value on the validation set is observed to determine whether the model is overfitting or underfitting. If the MSE value of the validation set no longer decreases, the training is stopped and the trained LSTM model is saved as the state evaluation channel. In this way, when new battery working status data is input, the LSTM model can accurately identify its health status and output the corresponding health status result.

[0032] When the working status of the target battery is collected, it is immediately input into the constructed status assessment channel. The channel deeply analyzes and processes the input data based on the learned knowledge and rules, so as to accurately identify the current health status of the target battery.

[0033] Determine the key features used to characterize the decline of the battery health state. These features usually include the decay ratio of battery capacity, the growth rate of internal resistance, and the rate of decrease of charge and discharge efficiency. By analyzing the health state data, the specific values ​​of these features are calculated using the corresponding algorithm. For the calculation of the battery capacity decay ratio, the actual capacity in the current health state and the initial capacity of the battery are used to calculate the difference, and then divided by the initial capacity to obtain the decay ratio. The internal resistance growth rate is the difference between the current measured internal resistance and the initial internal resistance of the battery. The difference is the growth rate. The rate of decrease of charge and discharge efficiency can be determined by calculating the slope by linear regression or other curve fitting methods using the charge and discharge efficiency data recorded at different time points or charge and discharge cycle numbers. After extracting these decay features, arrange them in chronological order or in the order of charge and discharge cycle numbers to construct time series data. Next, a suitable time series fitting method, the autoregressive moving average model (ARIMA), is used. The time series data needs to be tested for stationarity. If the data is not stationary, it is stabilized by methods such as difference. Then, the model parameters (such as p, d, q values) are determined based on the analysis results of the autocorrelation function (ACF) and the partial autocorrelation function (PACF), and the ARIMA model with determined parameters is used to fit the decay characteristic time series to obtain the fitting curve and the corresponding model parameter estimates. Finally, the decay data is generated based on the fitting results. These data include information such as the predicted value of the decay trend, the confidence interval, and the residual analysis of the model, so as to comprehensively reflect the decay trend and change law of the battery health status, and provide strong data support for the subsequent identification of the health stage based on the decay constraint.

[0034] After the generation of the decay data is completed, the healthy phase based on decay constraint identification is entered. A series of strict decay constraint conditions are pre-set. These conditions are determined after a large number of experimental studies, industry experience accumulation and in-depth analysis of the performance change laws of the same type of batteries. For example, when the battery capacity decay ratio exceeds 20% and the internal resistance growth reaches 50% of the initial internal resistance, it is considered to be in the moderate decay phase; if the capacity decay ratio exceeds 35% and the charge and discharge efficiency reduction rate exceeds a certain threshold, it will be judged to be in the severe decay phase. The generated decay data is compared and analyzed one by one with these preset decay constraint conditions. The data analysis algorithm will carefully check whether the various indicators in the decay data meet the condition requirements of the corresponding stage. If the decay data shows that the battery's capacity decay, internal resistance change and charge and discharge efficiency are all at a low level and do not touch the constraint boundary of any decay stage, then the battery will be judged to be in the healthy stage. Conversely, once the decay data meets the constraint condition combination of a specific decay stage, the battery's health status will be quickly and accurately divided into the corresponding health stage, such as mild decay, moderate decay or severe decay. Through this rigorous identification process based on decay constraints, the health stage of the battery can be clearly and unambiguously determined, thus providing a crucial basis for the subsequent formulation of targeted battery maintenance and charging strategies, ensuring the safe and efficient operation of new energy vehicle batteries and extending their service life.

[0035] In a possible implementation, step S300 further includes: Step S310: extracting charging items of pre-charging parameters, and configuring a replenishment sequence of the charging items based on the life cycle position.

[0036] Step S320: Perform a performance test on the optimized charging item obtained by dynamically controlling the charging item to generate a charging performance.

[0037] Step S330: identifying the deficiency of the charging performance according to the ideal performance to obtain the performance to be supplemented, wherein the ideal performance is the charging performance of the battery sample at the beginning of the life cycle.

[0038] Step S340: performing charging parameter quantization processing on the to-be-replenished performance to obtain the to-be-replenished parameters.

[0039] Step S350: Distributing parameters to be replenished to the charging items according to the replenishment order of the charging items to generate parameters of the items to be replenished.

[0040] Step S360: performing charging control according to the parameters of the items to be supplemented to obtain the charging parameters.

[0041] Specifically, each charging item of the pre-charging parameters is accurately extracted from a preset database or standard parameter library. These charging items are based on the specific parameters such as current and voltage corresponding to an ideal healthy battery in normal operation. They represent the charging requirements of the battery in the best performance state. For example, the initial charging current of the battery in its ideal health state is set to 50A, the charging voltage is 400V, etc. These parameters form the basis for subsequent analysis and adjustment. Then, further operations are carried out based on the life cycle position of the target battery that has been determined. When the target battery is in the early stage of its life cycle, the battery performance is good at this time, and more emphasis is placed on fast charging to improve charging efficiency. Then the current parameter will be given a higher priority in the charging item supplement sequence; and when the battery is in the middle and late stages of its life cycle, as the battery performance gradually declines, in order to reduce damage to the battery and extend its service life, more attention is paid to voltage stability and charging safety. At this time, the priority of the voltage parameter will be increased accordingly. By comprehensively considering the characteristics and needs of the battery at different positions in its life cycle, the extracted charging items are comprehensively evaluated and sorted, so as to reasonably configure the charging item replenishment sequence that meets the current status of the target battery, ensuring accurate charging control based on the actual situation of the battery, and realizing maximum utilization of battery performance and effective extension of battery life.

[0042] A multivariable control algorithm is used to dynamically control and test the performance of charging items. First, the charging current, voltage and charging time are used as control variables, and different initial values ​​and change steps are set. For example, the charging current starts from the minimum value and gradually increases in a certain increment. The voltage is adjusted according to a specific proportion within the safety range, and the charging time is also set in segments accordingly. In the dynamic control process, the feedback control mechanism is used to monitor the state parameters of the battery in real time, such as battery temperature, voltage change rate and internal resistance. For the generated optimized charging items, a weighted evaluation algorithm is used to generate charging performance. Different weights are assigned to key indicators such as charging efficiency, battery temperature rise stability and capacity growth effect. The weighted sum of these indicators in different time periods during the entire charging process is calculated by integration. For example, if the charging efficiency weight is set to 0.4, the battery temperature rise stability weight is 0.3, and the capacity growth effect weight is 0.3. During the charging process, the data of these indicators are collected at regular time intervals, and their weighted sum is calculated. Finally, a value that comprehensively reflects the charging performance is obtained, so as to accurately evaluate the performance of the optimized charging items.

[0043] Detailed charging data of the same type of battery samples at the beginning of the life cycle are retrieved from the database to build an ideal performance model. This model covers the optimal values ​​or ideal ranges of key indicators such as charging efficiency, battery temperature rise range, charging time, and battery capacity recovery after charging. The current battery charging performance data obtained through actual testing is compared with the ideal performance model one by one. For the charging efficiency indicator, if the ideal model stipulates that the charging efficiency should reach more than 95% in a specific charging stage, but the actual test result is only 85%, it can be clearly seen that there is a problem of insufficient efficiency; in terms of battery temperature rise, if the battery temperature rise should be controlled within 10°C during the entire charging process under ideal conditions, but the actual temperature rise is monitored to reach 15°C, this indicates that there are defects in temperature control. Through this detailed comparative analysis, the difference calculation and statistical analysis methods are used to accurately identify the gap between the actual charging performance and the ideal performance. These gaps are summarized and classified to determine the aspects that need to be supplemented and improved, and finally form the performance to be supplemented.

[0044] In order to convert the performance to be supplemented into an operational parameter to be supplemented, different methods are used for different situations. If the performance to be supplemented is that the charging speed is too slow, first calculate the current charging current based on the battery capacity and the current charging time, and then combine the expected charging time to get the expected current. The difference between the two is the current parameter to be supplemented. If the battery heats up too much, use the heat conduction formula and Joule's law, combine the battery heat dissipation structure and the maximum allowable temperature and other factors to deduce and calculate the voltage corresponding to safe heating. The difference with the current voltage is the voltage parameter to be supplemented. When the battery capacity after charging does not meet expectations, with the help of the empirical formula of the battery charging capacity, first fix the current to calculate the required extended charging time, then adjust the current increment and recalculate the charging time through the iterative algorithm, and finally determine the current and charging time parameters to be supplemented, so as to achieve accurate quantification of the performance to be supplemented.

[0045] According to the determined charging item replenishment order, the parameters to be supplemented are distributed to each charging item. For example, if it is analyzed that under the current battery state, voltage stability is the most critical to improving battery performance, and it is at the top of the replenishment order, then the parameters to be supplemented related to voltage adjustment obtained through quantitative calculation will be preferentially allocated to the voltage charging item according to certain rules. For other charging items, such as current and charging time, the corresponding parameters to be supplemented will also be reasonably allocated according to their order in the replenishment order. In the allocation process, the interrelationships and synergies between the charging items will be fully considered to ensure that the distributed parameters can form an organic whole and jointly promote the optimization of battery charging effects.

[0046] The parameters to be supplemented are input into a dedicated battery charging controller, which generates corresponding control signals based on these parameters, which can accurately adjust the output of the charging device. For example, if the parameters to be supplemented specify that the charging current should be maintained at 30A in the initial stage, and then increase at a rate of 5A every 10 minutes until it reaches 50A and remains stable, the controller will send instructions to the converter according to this requirement to make it output the corresponding current. During the charging process, the voltage, current, temperature and other parameters of the battery are collected in real time through high-precision sensors installed on the battery pack. These parameters will be fed back to the controller, which will compare them with the preset safety range and performance indicators. If it is found that the actual current exceeds the specified error range, for example, the actual current reaches 55A, and the allowable error range is ±2A, the controller will immediately adjust the output of the converter to reduce the current and return it to the specified range. Through continuous cyclic monitoring and adjustment, it is ensured that the entire charging process strictly complies with the requirements of the parameters to be supplemented. After multiple charging cycles and data optimization, the charging parameters that can achieve the best charging effect under the current battery status are finally determined, thereby completing the conversion from the parameters to be supplemented to the final charging parameters, and realizing accurate and efficient charging control of new energy vehicle batteries.

[0047] In a possible implementation, step S310 further includes: Step S311: simulate positioning the battery sample according to the life cycle position to obtain a simulated battery sample assembled at the life cycle position.

[0048] Step S312: performing a performance simulation based on charging item control on the simulated battery sample to generate a simulated charging performance.

[0049] Step S313: performing a performance evaluation based on the simulated charging performance, mapping the charging items from high to low based on the evaluated performance, and generating the charging item replenishment sequence.

[0050] Specifically, the life cycle position of the battery sample is first determined, which usually relies on a variety of monitoring data, such as the number of battery charge and discharge cycles, usage time, and real-time battery health indicators continuously recorded by the battery management system (BMS), including battery internal resistance, capacity decay rate, etc. Once the life cycle stage is clear, professional battery simulation software, such as the battery model library in MATLAB / Simulink, is used to adjust various parameters according to the established battery aging model based on the chemical characteristics and initial parameters of the battery. For example, for battery samples in the life cycle decline period, the initial capacity is set to decay at a certain ratio (such as 20%) in the software according to experimental data or theoretical models, and the internal resistance is increased accordingly (such as 30%), so as to simulate the internal state of the battery that meets the life cycle position, thereby obtaining a simulated battery sample that accurately assembles the characteristics of the life cycle position.

[0051] To simulate the performance of the simulated battery samples based on charging item control, a simulated charging experimental platform needs to be built. The core component of this platform is a programmable DC power supply. By writing a specific control program, it can flexibly set constant current charging, constant voltage charging and various combined charging modes to accurately control the charging current and voltage. At the same time, high-precision temperature sensors and power monitoring equipment are installed on the simulated battery samples to collect real-time temperature changes and power growth data of the samples during the charging process. These data are transmitted to the computer via a data acquisition card to conduct a comprehensive analysis of the charging process under different charging item combinations. Multi-dimensional information such as the charging efficiency, the trend of the temperature curve, and the changes in the capacity growth curve are all taken into consideration, and a detailed simulated charging performance report is finally generated.

[0052] The pre-set evaluation index system is enabled, which covers key indicators such as charging efficiency, battery temperature stability, and capacity recovery speed, and different weights are assigned to each of them, such as 40% for charging efficiency, 30% for battery temperature stability, and 30% for capacity recovery speed. By writing an evaluation algorithm, the actual data of each indicator in the simulated charging performance is compared and calculated with the corresponding ideal value. For example, if the ideal value of charging efficiency is 95% and the actual value is 90%, the score of this item is calculated according to the weight. After the evaluation of the simulated charging performance of all charging items is completed, the scores are sorted from high to low. Then, the data mapping algorithm is used to closely associate the score ranking with the corresponding charging item, and a supplementary order of charging items arranged from high to low is generated, providing a clear priority basis for subsequent charging control.

[0053] In a possible implementation, step S312 further includes: Step S3121: Generate a charging item control strategy through charging item control.

[0054] Step S3122: extracting a first control strategy according to the charging item control strategy.

[0055] Step S3123: Perform performance simulation based on the first control strategy to generate a first simulated performance.

[0056] Step S3124: according to the first simulation performance, generate a second simulation performance, a third simulation performance, and finally an Nth simulation performance through the performance simulation of the charging item control strategy, wherein N is an integer greater than 1.

[0057] Step S3125: combining the first simulation performance, the second simulation performance, and the Nth simulation performance to generate the simulated charging performance.

[0058] Specifically, the various parameters of the simulated battery sample are comprehensively analyzed, including the battery capacity, internal resistance, current health status, and past charge and discharge data. At the same time, combined with the desired charging effects, such as fast charging, efficient charging, and maintaining stable battery temperature, professional battery charging theory and a large amount of experimental data experience are used to generate charging control strategies. For example, if the capacity of the simulated battery sample is large and the current health status is good, the constant current-constant voltage (CC-CV) charging strategy is preferred, and the current value of the initial constant current stage is set within a reasonable range according to the chemical characteristics and heat dissipation performance of the battery, such as 0.5C to 1C (C is the battery capacity multiple) to ensure that a higher charging speed is achieved without damaging the battery. When the internal resistance of the battery sample is high or the health status declines, it is adjusted to a multi-stage variable current charging strategy, and a lower current is used for pre-charging at the beginning of charging to protect the battery and improve the charging efficiency. The current and voltage are then dynamically adjusted according to the voltage and temperature changes of the battery to ensure the safety and effectiveness of the entire charging process. In this way, various factors are comprehensively considered to generate a charging control strategy suitable for the simulated battery sample.

[0059] Among the generated charging item control strategies, the first control strategy is extracted according to the priority and key performance indicators. For example, if the temperature stability of the current battery sample is the primary concern, then the constant current-constant voltage switching control strategy with better temperature control effect is extracted as the first control strategy. This strategy can effectively control the battery temperature rise rate while ensuring a certain charging efficiency.

[0060] The extracted first control strategy is applied to the charging process of the simulated battery sample, and the battery charging process under this strategy is simulated through simulation software. In this process, various parameters of the battery, such as charging current, voltage, battery temperature, power growth, etc., are monitored in real time. After a period of simulation, the first simulation performance is generated based on these monitoring data. For example, detailed performance data such as the battery charging efficiency reaches 80% in the first 30 minutes under this strategy, the battery temperature rises by 10°C, and the power is charged from 30% to 45%.

[0061] Based on the first simulation performance, the charging item control strategy is used again to simulate the performance of different parameter combinations or adjustments. For example, based on the first control strategy, the current value is fine-tuned or the voltage setting in the constant voltage stage is changed to generate the second simulation performance; the charging time interval or switching conditions are further changed to obtain the third simulation performance, and so on, until the Nth simulation performance is generated. Each simulation performance records the battery's charging efficiency, temperature changes, capacity growth, etc. under different strategy adjustments.

[0062] The first simulation performance, the second simulation performance, and the Nth simulation performance are comprehensively analyzed and integrated. By comparing key indicators such as charging efficiency, battery temperature stability, and capacity growth in different performances, abnormal data and unreasonable results are removed, and representative and reliable data are extracted to generate a comprehensive and accurate simulated charging performance. This simulated charging performance can reflect the comprehensive charging conditions of simulated battery samples under different charging item control strategies, providing important data support for subsequent evaluation and determination of the charging item replenishment sequence.

[0063] In a possible implementation, step S360 further includes: Step S361: According to the replenishment order of the charging items, the parameters of the items to be replenished are controlled in sequence based on the charging item control limit to obtain true replenishment item parameters.

[0064] Step S362: Perform performance evaluation on the cumulative control of the true supplementary item parameter to obtain the cumulative performance.

[0065] Step S363: If the accumulated performance satisfies the ideal performance, the charging control of the parameter to be supplemented is stopped to obtain the charging parameter.

[0066] Specifically, the charging item supplement order and the corresponding parameters of the items to be supplemented are imported into the charging control system. For each charging item, the parameters of the items to be supplemented are read in sequence according to the supplement order, and compared with the charging item control limits pre-stored in the system database. Taking the charging current as an example, if the parameter of the item to be supplemented at a certain stage is a charging current that needs to be increased by 0.3C (C is the battery capacity multiple), and the current control limit of the battery type in the current charging state is set to 0.2C-0.5C, it is judged that this parameter is within the limit range, and then the output of the converter is adjusted to accurately increase the charging current by 0.3C to achieve the control of this parameter to be supplemented. During the whole process, the adjustment of each charging item parameter is continuously monitored to ensure that it strictly follows the charging item control limit. Once a parameter exceeds the limit, the alarm mechanism is immediately triggered, and the adjustment of the parameter is stopped, and the abnormal information is recorded. Through such a series of rigorous operations, all parameters of the items to be supplemented are controlled in sequence, and finally the true supplementary item parameters that meet the requirements are obtained, providing an accurate control basis for the subsequent charging process.

[0067] In the circuit connecting the charging device and the battery, high-precision sensors, including current sensors, voltage sensors, temperature sensors, etc., are installed. These sensors can collect various key data of the battery in the charging process in real time and accurately. The data collected by these sensors are evaluated using a data acquisition card. During the evaluation process, the software will calculate the charging efficiency of the battery based on the setting of the true supplementary item parameters and the data changes in the actual charging process. For example, the charging efficiency is determined by comparing the ratio of the actual amount of electricity charged into the battery to the theoretical amount of electricity that should be charged. At the same time, the battery temperature change curve over time is analyzed to determine whether the temperature is within the safe range and whether the temperature change is stable. If the temperature rises too fast or exceeds the preset safe temperature range, a corresponding warning mark will be made in the evaluation result. In addition, the battery capacity growth will be monitored and analyzed, and the effect of the true supplementary item parameters on the battery capacity recovery will be evaluated by comparing the battery capacity changes before and after charging and the difference with the expected capacity growth. Based on the above evaluation results of charging efficiency, temperature stability, capacity growth, etc., a cumulative performance report that fully reflects the cumulative control effect of the true supplementary item parameters is generated.

[0068] If the evaluation finds that the cumulative performance has met the ideal performance of the battery sample at the beginning of its life cycle, it means that the current charging control has achieved the expected effect. At this time, immediately stop further charging control operations on the parameters to be supplemented and determine the current charging parameters. These finalized charging parameters can not only ensure the efficiency and safety of the battery during this charging process, but also fully consider the health status and service life of the battery, providing strong support for the continuous and stable operation of new energy vehicles.

[0069] In a possible implementation, step S500 further includes: Step S510: Obtaining natural influencing factors of the driving behavior record and the charging record according to the predicted life cycle position.

[0070] Step S520: Analyze the failure probability obtained based on the predicted health stage to obtain the failure influencing factors of the driving behavior record and the charging record.

[0071] Step S530: Generate the maintenance suggestion by combining the natural influencing factors and the fault influencing factors.

[0072] Specifically, first read and parse the stored information about the predicted life cycle position of the battery. Then, extract the driving behavior record and charging record data from the vehicle's onboard computer and charging management system. For driving behavior records, analyze the duration and frequency of acceleration, deceleration, and constant speed driving, as well as the speed change curve. If it is in the predicted life cycle position where the battery performance declines rapidly, frequent rapid acceleration will greatly increase the vehicle speed in a short period of time (such as rapid acceleration from 40km / h to 90km / h in 5 minutes). This high-intensity discharge behavior has a greater impact on the battery and is an important natural influencing factor. In terms of charging records, pay attention to the charging time, location, charging pile type, and ambient temperature. If it is predicted that the battery is about to enter a sensitive aging stage, charging in a high temperature environment (such as above 45°C) will significantly accelerate battery aging, and this factor will be marked as a key factor. By combining driving and charging behavior data with the predicted life cycle position for analysis, the natural influencing factors related to the position are obtained, providing a key basis for subsequent battery maintenance.

[0073] Build a battery failure analysis system based on big data and machine learning algorithms to collect a large amount of historical data of the same type of batteries under different driving and charging conditions, including various performance indicators of the battery, corresponding driving behavior details (such as acceleration, deceleration, driving time, road conditions, etc.) and charging record information (such as charging voltage, current, charging time, charging environment temperature, etc.). Use these data to train the neural network model. During the training process, use the different health stages of the battery as labels to let the model learn the complex relationship between different driving and charging behaviors and the probability of battery failure. After obtaining the predicted health stage of the battery, input it into the trained model. The model will output various types of failure probabilities such as battery short circuit risk probability and electrolyte leakage risk probability based on the learned rules. Subsequently, for each failure probability, the driving behavior records and charging records are sorted out in detail. For example, if the model indicates that the probability of battery short circuit risk increases, check whether there is a record of the vehicle being hit or bumped violently in the driving behavior, and whether there is a loose or poor contact situation in the charging record. If there is an increase in the probability of electrolyte leakage risk, check whether the battery has been overcharged or charged in an extreme temperature environment, and whether the battery has been in a high temperature state for a long time during driving. In this way, the failure influencing factors in the driving behavior records and charging records can be accurately discovered, laying the foundation for the generation of subsequent maintenance recommendations.

[0074] Establish a maintenance strategy database that includes various natural influencing factors and fault influencing factors. This database is based on a large amount of battery research data and actual application cases. For natural influencing factors, if the driving behavior record shows frequent sudden acceleration, extract corresponding suggestions from the database, such as suggesting that the driver maintain a stable driving style and avoid frequent sudden acceleration operations to reduce the instantaneous discharge pressure of the battery and extend the battery life. The specific operation can be to slowly accelerate when starting and overtaking, and control the acceleration time within a reasonable range. If the charging record shows that charging is often carried out in a high temperature environment, it is recommended to choose to charge in an indoor parking lot with suitable temperature or a charging pile with shading facilities as much as possible, and avoid charging outdoors in direct sunlight and high temperature. If a high temperature environment cannot be avoided, consider using a battery cooling device or waiting for the battery temperature to drop before charging. For fault influencing factors, if it is analyzed that the battery capacity decay is too fast due to frequent incomplete charging, it is recommended to charge to 90%-100% of the battery capacity each time, and avoid long-term shallow charging and shallow discharge to maintain the healthy capacity of the battery. At the same time, perform a deep discharge and full charge regularly to calibrate the battery power display. If it is found that the increase in battery internal resistance is caused by the use of an incompatible charging pile, it is recommended to ensure that the charging pile is used to charge the vehicle. You can check the vehicle manual or consult the car manufacturer for the recommended charging pile brand and model. Before charging, check whether the output voltage and current of the charging pile are stable. By combining these specific suggestions for different influencing factors, a complete maintenance recommendation report is generated and provided to the driver or vehicle maintenance personnel to ensure that the battery is in good working condition.

[0075] Embodiment 2 is based on the same inventive concept as the new energy vehicle charging control method combined with battery health monitoring in the previous embodiment. Figure 2 As shown, the present application provides a new energy vehicle charging control system combined with battery health monitoring, and the system and method embodiments in the present application embodiments are based on the same inventive concept. Among them, the system includes: The health stage acquisition module 10 is used to collect the working state of the target battery, perform health assessment on the working state based on the state assessment channel, perform decay analysis and stage division on the generated health state, and obtain a health stage.

[0076] The life cycle position generating module 20 is used to extract battery samples of the same type as the target battery, evaluate the life cycle of the battery samples from the beginning to the end of life, locate the health stage in the life cycle, and generate the life cycle position.

[0077] The charging parameter acquisition module 30 is used to perform progressive charging control on the target battery according to the life cycle position to obtain charging parameters.

[0078] The predicted life cycle position generation module 40 is used to collect driving behavior records and charging records for health assessment, decay analysis and stage division, locate the generated predicted health stage in the life cycle, and generate a predicted life cycle position.

[0079] The maintenance suggestion generating module 50 is used to perform a preconditioning judgment on the predicted life cycle position at the life cycle position, and if a preconditioning result is obtained, analyze the preconditioning influencing factors to generate maintenance suggestions.

[0080] Furthermore, the health stage acquisition module 10 further includes: The state sample acquisition unit is used to acquire the state sample and the health state sample of the battery sample.

[0081] The state evaluation channel establishing unit is used to train the state samples and the health state samples based on the neural network architecture to establish the state evaluation channel.

[0082] The health status identification unit is used to input the working status into the status evaluation channel to perform health status identification to obtain the health status.

[0083] The decay data generating unit is used to extract decay features of the health state, perform trend analysis of time series fitting based on the decay features, and generate decay data.

[0084] The decay data identification unit is used to identify the decay data based on the decay constraint to obtain the healthy stage.

[0085] Furthermore, the charging parameter acquisition module 30 also includes: The charging item replenishment sequence configuration unit is used to extract the charging items of the pre-charging parameters and configure the charging item replenishment sequence based on the life cycle position.

[0086] The charging performance generating unit is used to perform a performance test on the optimized charging item obtained by dynamically controlling the charging item to generate a charging performance.

[0087] The to-be-supplemented performance acquisition unit is used to identify the deficiency of the charging performance according to the ideal performance to obtain the to-be-supplemented performance, wherein the ideal performance is the charging performance of the battery sample at the beginning of the life cycle.

[0088] The to-be-replenished parameter acquisition unit is used to perform charging parameter quantization processing on the to-be-replenished performance to obtain the to-be-replenished parameter.

[0089] The to-be-supplemented item parameter acquisition unit is used to distribute the to-be-supplemented parameters to the charging items according to the charging item replenishment sequence to generate the to-be-supplemented item parameters.

[0090] A charging control unit is used to perform charging control according to the parameters of the items to be supplemented to obtain the charging parameters.

[0091] Furthermore, the charging item supplement sequence configuration unit also includes: The simulated battery sample acquisition unit is used to simulate the positioning of the battery sample according to the life cycle position to obtain the simulated battery sample assembled at the life cycle position.

[0092] The simulated charging performance generating unit is used to perform a performance simulation based on the charging item control on the simulated battery sample to generate a simulated charging performance.

[0093] An evaluation performance mapping unit is used to perform performance evaluation based on the simulated charging performance, map the charging items from high to low based on the evaluation performance, and generate the charging item replenishment sequence.

[0094] Furthermore, the simulated charging performance generating unit further includes: The charging item control strategy generating unit is used to generate a charging item control strategy through charging item control.

[0095] The first control strategy extraction unit is used to extract a first control strategy according to the charging item control strategy.

[0096] The first simulation performance generating unit is used to perform performance simulation based on the first control strategy to generate a first simulation performance.

[0097] The Nth simulation performance generating unit is used to generate a second simulation performance, a third simulation performance, and finally an Nth simulation performance through the performance simulation of the charging item control strategy according to the first simulation performance, wherein N is an integer greater than 1.

[0098] The simulation performance combining unit is used to combine the first simulation performance, the second simulation performance to the Nth simulation performance to generate the simulation charging performance.

[0099] Furthermore, the charging control unit further includes: The true supplementary item parameter obtaining unit is used to control the parameters of the items to be supplemented in sequence according to the replenishment sequence of the charging items and based on the charging item control limit to obtain the true supplementary item parameters.

[0100] The cumulative performance acquisition unit is used to perform performance evaluation on the cumulative control of the true supplementary item parameter to obtain the cumulative performance.

[0101] The ideal performance acquisition unit is used to stop the charging control of the parameter to be supplemented if the accumulated performance meets the ideal performance, so as to obtain the charging parameter.

[0102] Furthermore, the maintenance suggestion generating module 50 further includes: The natural influencing factor acquisition unit is used to acquire the natural influencing factors of the driving behavior record and the charging record according to the predicted life cycle position.

[0103] The fault influencing factor acquisition unit is used to analyze the fault probability obtained based on the predicted health stage to obtain the fault influencing factors of the driving behavior record and the charging record.

[0104] The influencing factor combining unit is used to combine the natural influencing factor and the fault influencing factor to generate the maintenance suggestion.

[0105] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0106] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0107] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A new energy vehicle charging control method combined with battery health monitoring, characterized in that: include: Collecting the working state of the target battery, performing health assessment on the working state based on the state assessment channel, performing decay analysis and stage division on the generated health state, and obtaining a health stage; Extracting battery samples of the same type as the target battery, evaluating the life cycle of the battery samples from the beginning of life to the end of life, locating the health stage in the life cycle, and generating a life cycle position; Performing progressive charging control on the target battery according to the life cycle position to obtain charging parameters; Collect driving behavior records and charging records for health assessment, decay analysis and stage division, locate the generated predicted health stage in the life cycle, and generate a predicted life cycle position; A preconditioning judgment is performed on the predicted life cycle position at the life cycle position, and if a preconditioning result is obtained, a preconditioning influencing factor is analyzed to generate a maintenance suggestion.

2. The new energy vehicle charging control method combined with battery health monitoring as claimed in claim 1 is characterized in that: Performing a health assessment on the working state based on the state assessment channel, performing a decay analysis and stage division on the generated health state, and obtaining a health stage, including: Obtaining a state sample and a health state sample of the battery sample; Training the state samples and health state samples based on a neural network architecture to establish the state evaluation channel; Inputting the working status into the status evaluation channel to identify the health status and obtain the health status; Extracting decay features of the health state, performing trend analysis of time series fitting based on the decay features, and generating decay data; The decay data is identified based on decay constraints to obtain the healthy stage.

3. The new energy vehicle charging control method combined with battery health monitoring as claimed in claim 1, characterized in that: The target battery is progressively charged according to the life cycle position to obtain charging parameters, including: Extracting charging items of pre-charging parameters, and configuring a replenishment sequence of the charging items based on the life cycle position; Performing a performance test on the optimized charging item obtained by dynamically controlling the charging item to generate a charging performance; Identify the deficiency of the charging performance according to the ideal performance to obtain the performance to be supplemented, wherein the ideal performance is the charging performance of the battery sample at the beginning of the life cycle; Performing charging parameter quantization processing on the to-be-replenished performance to obtain a to-be-replenished parameter; Distribute parameters to be supplemented to the charging items according to the replenishment order of the charging items, and generate parameters of items to be supplemented; The charging control is performed according to the parameters of the item to be supplemented to obtain the charging parameters.

4. The new energy vehicle charging control method combined with battery health monitoring as claimed in claim 3 is characterized in that: Configuring a charging item replenishment sequence based on the life cycle position includes: Simulate positioning the battery sample according to the life cycle position to obtain a simulated battery sample assembled at the life cycle position; Performing a performance simulation based on charging item control on the simulated battery sample to generate a simulated charging performance; A performance evaluation is performed based on the simulated charging performance, and the charging items are mapped from high to low based on the evaluated performance to generate the charging item replenishment sequence.

5. The new energy vehicle charging control method combined with battery health monitoring as claimed in claim 4 is characterized in that: The simulated battery sample is subjected to a performance simulation based on the charging item control to generate a simulated charging performance, including: Generate charging item control strategy through charging item control; Extracting a first control strategy according to the charging item control strategy; Performing a performance simulation based on the first control strategy to generate a first simulated performance; According to the first simulation performance, generating a second simulation performance, a third simulation performance, and finally an Nth simulation performance through the performance simulation of the charging item control strategy, wherein N is an integer greater than 1; The simulated charging performance is generated by combining the first simulation performance, the second simulation performance, and the Nth simulation performance.

6. The new energy vehicle charging control method combined with battery health monitoring as claimed in claim 3 is characterized in that: The charging control is performed according to the parameter to be supplemented to obtain the charging parameter, including: According to the replenishment order of the charging items, the parameters of the items to be replenished are controlled in sequence based on the charging item control limit to obtain true replenishment item parameters; Performing performance evaluation on the cumulative control of the true supplementary item parameter to obtain a cumulative performance; If the accumulated performance meets the ideal performance, the charging control of the parameter to be supplemented is stopped to obtain the charging parameter.

7. The new energy vehicle charging control method combined with battery health monitoring as claimed in claim 1, characterized in that: Analyze the pre-emptive influencing factors and generate maintenance suggestions, including: Obtaining natural influencing factors of the driving behavior record and the charging record according to the predicted life cycle position; Analyze the failure probability obtained based on the predicted health stage to obtain the failure influencing factors of the driving behavior record and the charging record; The maintenance suggestion is generated by combining the natural influencing factors and the fault influencing factors.

8. A new energy vehicle charging control system combined with battery health monitoring, characterized in that: The system is used to implement the new energy vehicle charging control method combined with battery health monitoring according to any one of claims 1 to 7, and the system includes: A health stage acquisition module is used to collect the working state of the target battery, perform health assessment on the working state based on the state assessment channel, perform decay analysis and stage division on the generated health state, and obtain a health stage; A life cycle position generation module, used to extract battery samples of the same type as the target battery, evaluate the life cycle of the battery samples from the beginning of life to the end of life, locate the health stage in the life cycle, and generate a life cycle position; A charging parameter acquisition module, used to perform progressive charging control on the target battery according to the life cycle position to obtain charging parameters; A predicted life cycle position generation module is used to collect driving behavior records and charging records for health assessment, decay analysis and stage division, locate the generated predicted health stage in the life cycle, and generate a predicted life cycle position; The maintenance suggestion generating module is used to make a pre-condition judgment on the predicted life cycle position at the life cycle position, and if a pre-condition result is obtained, analyze the pre-condition influencing factors to generate maintenance suggestions.

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